Senior Software Development Engineer, Core Search

The Amazon Search Navigation AI team helps customers go from a broad search intent to the few products that fit their needs, so they can make a confident purchase decision. We generate highly relevant, context-aware, and personalized search refinements. We also decide the order they appear in across Amazon's Search Navigation experiences, guiding customers to the most relevant results. The team owns Search Navigation's P0 metrics and defect guardrails, which keep performance and reliability high for hundreds of millions of customers. We are reinventing search refinement with generative AI. Our recently launched Smart Refinement experience uses large language models (LLMs) served online to turn product specifications into natural-language suggestions that match how customers think, such as "for colder weather" or "lightweight for travel." Each suggestion is personalized to the customer's context. Next, we are building multi-turn, conversational refinement, where each interaction builds on the last and helps customers narrow their intent in fewer steps. We build and run the engineering systems behind this work, including the Navigation Experience Service (NES). NES connects to Amazon's core search systems, serves more than 3 billion queries worldwide every day, and runs real-time LLM inference at global scale under strict latency and cost limits. Our platform supports launches of new navigation content and UIs, and it makes fast offline and online experimentation possible. We are looking for a Senior Software Development Engineer to be a technical leader on the team. You will shape the architecture of our Search Navigation platform, including the systems that serve LLMs in real time, lead complex projects that span multiple teams, and work closely with Applied Scientists to take LLM innovations from research into production. You are comfortable with ambiguity, you set a high bar for engineering, and you are curious about applied science. Key job responsibilities - Lead the architecture, design, and delivery of large-scale, distributed, low-latency services that power Amazon Search Navigation experiences worldwide - Own the technical direction for online LLM serving in Search Navigation, including GPU and accelerator inference, request-time personalization, asynchronous rendering, caching, and cost and latency optimization - Drive the engineering roadmap for Smart Refinement and multi-turn conversational refinement, scaling from initial launch to a much larger share of search traffic - Build safe, reliable GenAI experiences with layered safeguards: input triggering and exclusion controls, output validation and filtering, and graceful fallbacks - Partner with Applied Scientists, Product Managers, and Program Managers to define requirements and bring models (including distilled LLMs) into production - Design and build scalable data pipelines that process petabytes of search and behavioral data for feature generation, model training, distillation, and evaluation - Drive experimentation strategy: design, run, and analyze online A/B experiments, and make data-driven launch recommendations - Own operational excellence for critical services, including P0 metrics, defect guardrails, monitoring, on-call health, and availability and latency goals - Lead cross-team initiatives, influence the technical decisions of partner teams across Search, and mentor engineers while raising the engineering bar Minimum qualifications - 5+ years of non-internship professional software development experience - 5+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience as a mentor, tech lead or leading an engineering team Preferred qualifications - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Bachelor's degree in computer science or equivalent - Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution - Experience leading and influencing your team or organization, or experience as a mentor, tech lead or leading an engineering team - Experience building real-time, low-latency ML or LLM inference systems at scale, including optimizing for latency, throughput, and cost, and developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware - Experience building and operating large-scale distributed services (e.g., search, recommendations, or ads serving) handling billions of requests per day Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually